Automated pressure ulcer dimension measurements using a depth camera

The purpose of this research was to develop an automatic wound segmentation method for a pressure ulcer (PU) monitoring system (PrUMS) using a depth camera to provide automated, non-contact wound measurements.

The automatic wound segmentation method, which combines multiple convolutional neural network classifiers, was developed to segment the wound region to improve PrUMS accuracy and to avoid the biased decision from a single classifier. Measurements from PrUMS were compared with the standardised manual measurements (ground truth) of two clinically trained wound care nurses for each wound.

Compared to the average ground truth measurement (38×34×15mm), measurement errors for length, width and depth were 9.27mm, 5.89mm and 5.79mm, respectively, for the automatic segmentation method, and 4.72mm, 4.34mm, and 5.71mm, respectively, for the semi-automatic segmentation method. There were no significant differences between the segmentation methods and ground truth measurements for length and width; however, the depth measurement was significantly different (p<0.001) from the ground truth measurement.

The novel PrUMS device used in this study provided objective, non-contact wound measurement and was demonstrated to be usable in clinical wound care practice. Images taken with a regular camera can improve the classifier's performance. With a dataset of 70 PUs for single and multiple (four images per PU) measurements, the differences between length and width measurements of the PrUMS and the manual measurement by nurses were not statistically significant (p>0.05). A statistical difference (p=0.04) was found between depth measurements obtained manually and with PrUMS, due to limitations of the depth camera within PrUMS, causing missing depth measurements for small wounds.